Characteristics of Low-Wage Workers and Their Labor Market Experiences: Evidence from the Mid- to Late 1990s. Findings From The Univariate Analysis

04/30/2004

To a large extent, and not surprisingly, the patterns of subgroup findings for the wage growth analyses are fairly similar to the patterns of subgroup findings for the aggregate analysis. We find some broad differences in labor market outcomes across key subgroups of the low-wage population, although the differences are smaller than expected (Tables VI.3 and VI.4). Males, older workers, educated workers, whites, and those without health limitations were somewhat more likely to experience wage growth than their respective counterparts. Job characteristics also matter  those who start with better jobs (measured by higher initial wages, availability of health benefits, and full-time work status) were more likely to experience wage growth than those in lower-quality jobs. We find few differences across occupations and industry. The exception is males in professional occupations and females in clerical and administrative support occupations  both groups were more likely to experience greater amounts of wage growth than workers in other occupations.

a. Findings for Subgroups Defined by Individual and Household Characteristics

Table V.3 presents our findings for subgroups defined by individual and household characteristics at the start of the low-wage job. We summarize these findings here:

  • Male low-wage workers were more likely than female low-wage workers to experience wage growth. Male low-wage workers were more likely than females to have earned at least $10 per hour at the end of the three-year follow-up period (30 percent, compared to 18 percent for females). They were also more likely to be in medium- or high-wage jobs (53 percent of males, compared to 40 percent of females). Finally, males were somewhat more likely to have experienced a relatively large increase in wages over time; 26 percent of males experienced a wage growth of more than 50 percent during a three-year follow-up period, compared with 20 percent of females. These gender results hold across all subgroups.
  • Males older than age 20 experienced greater wage growth than younger males. In our sample, teenage male workers experienced the lowest amounts of wage growth; only about 19 percent had wages over $10 per hour three and a half years after job start, compared with between 30 and 40 percent for older males. We observe similar patterns for other measures of wage growth for young males. We do not observe much difference in patterns of wage growth by age for females, however.
  • White females were likely to have the best wage growth outcomes, and Hispanic female workers were likely to have the poorest wage growth outcomes. Across all the measures of wage growth we examined, white females were most likely to experience the greatest growth, followed by black females. For example, 20 percent of white females earned more than $10 per hour about 42 months after job start, compared with 15 percent of blacks and only 10 percent of Hispanics. Similarly, white females were also somewhat more likely than females from other race/ethnic groups to have experienced wage growth of over 50 percent during a three-year follow-up period. We do not observe differences in patterns of outcomes for males by race/ethnicity.

 

Table V.3.
Measures Of Wage Progression After Job Start For Subgroups Of Low-Wage Workers
Defined By Individual And Household Characteristics At Job Start
(Percentages)
Subgroup Male Low-Wage Workers Female Low-Wage Workers
Earned More than $10 in Last Period In Medium- or High-Wage Jobs in Last Period More than 50 Percent Increase in Wages Earned More than $10 in Last Period In Medium- or High-Wage Jobs in Last Period More than 50 Percent Increase in Wages
   Overall 30 53 26 18 40 20
Age (in Years)
   Younger than 20 19 37 18 19 35 26
   20 to 29 29 57 27 18 45 21
   30 to 39 35 57 27 18 42 20
   40 to 49 32 54 26 17 33 18
   50 or older 38 45 30 20 35 21
Race/Ethnicity
   White and other non-Hispanic 32 57 25 20 43 21
   Black, non-Hispanic 26 46 30 15 36 19
   Hispanic 35 42 28 10 30 17
Educational Attainment
   Less than high school/GED 18 40 19 9 23 14
   High school/GED 26 52 22 15 35 18
   Some college 44 66 33 22 53 24
   College graduate or more 49 61 42 33 56 33
Has a Health Limitation
   Yes 23 45 21 11 41 26
   No 31 54 26 18 35 20
Household Type
   Single parent with children 30 56 30 15 35 19
   Married couple with children 31 53 24 20 41 23
   Married couple without children 32 49 23 16 38 18
   Other adults without children 29 56 28 20 51 21
Household Income as a Percentage of the Federal Poverty Level
    100 percent or less 26 49 29 10 32 17
   101 to 200 percent 30 49 22 14 32 17
   More than 200 percent 33 57 26 23 48 23
Full Sample Size 491 491 491 693 693 636
Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after the start of the panel period. All workers were followed for three years after job start.
Note: All figures are weighted using the longitudinal panel weight.
  • Education is strongly associated with wage growth. As expected, wage growth outcomes were typically poorest for high school dropouts and improved with education level. Among male low-wage workers, only 18 percent of those who were high school dropouts at the start of their jobs had hourly wages over $10 three and a half years later, compared with 26 percent of those with a high school credential and around 45 to 50 percent among those who attended college. Similarly, males with lower education levels were less likely to experience substantial wage growth. We find similar patterns for female workers.
  • Male low-wage workers with health limitations were somewhat less likely than those without health problems to experience higher levels of wage growth. Around 23 percent of low-wage male workers with health problems had wages of over $10 per hour at the end of the follow-up period, compared with just over 30 percent of those without health problems. While we observe modest differences in this direction for all measures for males, we do not observe similar patterns for females across all measures of wage growth. These findings are in contrast to the findings from Chapter IV, where we observed better labor market outcomes for those with no health limitations. These findings may be explained partly by the fact that those with health limitation are less likely to be employed at a later time and thus are less likely to be part of the wage growth sample.
  • We do not observe strong patterns of wage growth by household type for either male or female low-wage workers. Among females in our sample, single parents with children and married couples without children were somewhat less likely to experience greater wage growth than other household types. However, the differences were not large. Furthermore, we did not observe any such patterns of wage growth by household types for male workers.

    Poverty status is inversely associated with positive wage outcomes at followup. In general, low-wage workers in wealthier households were more likely than those in poorer households to experience greater wage growth. These findings may reflect the fact that those in wealthier households are also likely to be more educated, which may be related to the higher amounts of wage growth they experience. Interestingly, we find the reverse pattern for males who experienced wage growth of more than 50 percent. Males in households with income below the federal poverty level were more likely than males in other households to experience large increases in their wages.

b. Findings for Subgroups Defined by Job Characteristics

Our findings for subgroups defined by job characteristics at the start of the low-wage job indicate that job quality matters  those who started with better jobs tended to have jobs with somewhat higher hourly wages at the time of the follow-up period. However, fewer initial job characteristics are associated with who is most likely to experience a more than 50 percent wage growth. The exception is initial wages, and those with very low initial wages were most likely to experience the maximum increase in their wages over time (Table V.4). We summarize these results here:

  • In general, those with higher initial wages were more likely than those earning lower wages to earn more than $10 per hour at the end of the follow-up period. Sample members who started at less than $6 per hour were less likely to be earning more than $10 per hour at the end of the follow-up period or to have moved into a medium- or high-wage job. While they were less likely to exit the low-wage labor market, the lowest-wage workers (those earning less than $5 per hour) were more likely to experience the largest gains in their own wages over time. For example, 34 percent of male low-wage workers who had initial hourly wages of less than $5 were likely to have experienced a wage increase of over 50 percent three years later, compared with only around 20 percent among those whose starting wage was $6 per hour or more. We found similar patterns for female low-wage workers.
  • Male low-wage workers working more than 40 hours per week had higher hourly wages at followup than those working fewer hours. During the mid- to late 1990s, male low-wage workers who reported working more than 40 hours per week (about 20 percent of all workers) were more likely to be earning more than $10 per hour or have moved into a medium- or high-wage job three years later. For example, 42 percent of males who worked more than 40 hours per week had earned more than $10 per hour three and a half years after initial job start, compared with between 25 and 30 percent for workers who had worked fewer hours. The patterns are not as strong for female workers or for the percentage experiencing more than 50 percent wage growth for either gender.
  • Those in jobs that offered fringe benefits were somewhat more likely to have greater hourly wages three and a half years after initial job start. Those covered by health benefits (about one-third to half of all low-wage workers) were more likely than those not covered to have earned more than $10 in the last period (38 percent, compared to 26 percent for males, and 23 percent, compared to 13 percent for females). Health insurance coverage, however, did not seem to affect the percentage of male and female workers experiencing 50 percent wage growth.
  • Business owners were more likely than job holders to experience greater wage growth. Although business owners (about 13 percent of all low-wage workers) tended to have lower hourly wages than job holders near the start of their employment spells, they were more likely than job holders to experience greater amounts of wage growth. For example, 46 percent of low-wage male business owners earned more than $10 at the last period, compared to 29 percent for male job holders. We observe similar patterns of outcomes for female business owners and job holders, but the differences are smaller.
  • Among male low-wage workers, those in professional occupations experienced more wage growth than other workers. During the mid- to late 1990s, male low-wage workers who worked in professional occupations (eight percent of workers) were most likely to be in a medium- or high-wage job at the time of the followup, and those in service professions, handlers, and cleaners were the least likely to have escaped the low-wage labor market. We do not see patterns quite as strong for females, nor do we see strong patterns by industry type.

 

Table V.4.
Measures Of Wage Progression After Job Start For Subgroups Of Low-Wage Workers
Defined By Initial Job Characteristics
(Percentages)
Subgroup Male Low-Wage Workers Female Low-Wage Workers
Earned More than $10 in Last Period In Medium- or High-Wage Jobs in Last Period More than 50 Percent Increase in Wages Earned More than $10 in Last Period In Medium- or High-Wage Jobs in Last Period More than 50 Percent Increase in Wages
Overall 30 53 26 18 40 20
Hourly Wages
   Less than $5.00 24 39 34 13 25 27
   $5.00 to $5.99 20 39 28 12 33 17
   $6.00 to $6.99 35 63 20 25 54 20
   $7.00 or more 46 76 22 30 66 15
Hours Worked per Week
   1 to 19 25 35 20 15 31 18
   20 to 34 31 47 30 22 42 24
   35 to 40 27 54 22 16 42 18
   More than 40 42 61 33 15 44 27
Weekly Earnings
   Less than $150 31 42 33 18 34 24
   $150 to $299 24 52 23 17 44 19
   $300 to $600 59 74 29 25 47 15
Owns Business (Self-Employed)
   Yes 46 69 47 24 40 27
   No 29 52 24 17 41 20
Health Insurance Coverage(a)
   Yes 38 61 28 23 47 21
   No 26 49 25 13 35 20
Occupation
   Professional/technical 48 64 34 26 46 25
   Sales/retail 38 59 25 23 49 28
   Administrative support/clerical 35 59 36 23 58 17
   Service professions/handlers/cleaners 23 43 22 13 30 18
   Machine/construction/production/
transportation
32 61 25 15 29 20
   Farm/agricultural/other workers 27 45 29 3 30 16
Industry
   Agriculture/forestry/fishing/hunting/other 35 54 36 16 28 19
   Mining/manufacturing/construction/ transportation/utilities 29 57 24 12 30 16
   Wholesale/retail trade 30 49 21 16 41 25
   Personal/health/other services 30 53 28 21 45 19
Employment Status
   Continuously employed with one job 26 52 20 10 37 11
   Continuously employed with multiple jobs 35 62 29 20 48 21
   Intermittent, employed less than 75% of time 17 27 21 14 29 21
   Intermittent, employed 75% or more of time 34 57 27 22 44 25
Full Sample Size 491 491 460 693 693 636
Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after the start of the panel period. All workers were followed for 42 months after job start.
Note: All figures are weighted using the longitudinal panel weight.
a. These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from other sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall health insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health insurance variable could not always be linked to the job under investigation, which led to a significant number of missing values. However, the subsets of health insurance variables overlap considerably: the source of health insurance coverage was the employer for 80 percent of those with any coverage.
  • Time spent employed was associated with wage growth. For instance, about 33 percent of male workers who were employed for most of the period (at least 75 percent of months) earned at least $10 per hour at the end of the follow-up, compared to only 17 percent of males who were employed for fewer months (Table V.4). The corresponding figures for females are 19 percent and 14 percent, respectively. Thus, policies that promote employment retention could improve the wage growth of low-wage workers.
  • Among those continuously employed, those who switched jobs experienced greater wage growth than those who remained in the same job over the entire follow-up period. Workers who were continuously employed, but in different jobs, were somewhat more likely than those who remained employed in the same job to experience greater wage growth. For example, 35 percent of male workers who switched directly from one job to another were likely to earn more than $10 per hour at the end of the three-year follow-up period, compared with 26 percent of those who remained with the same employer over time (Table V.4). We find similar patterns even among intermittent workers who were employed at least 75 percent of the time over the three-year period. We find similar patterns of findings for female workers as well. These findings are consistent with the findings of Gladden and Taber (2000b) who find positive wage growth with job turnover, when workers moved directly between jobs or were unemployed for a short time.

2. Findings from the Multivariate Analysis

Thus far, we have examined subgroup results one at a time. However, many of these subgroups are correlated with each other. For example, we have seen that less disadvantaged workers and those in higher-quality jobs tend to have more positive wage growth outcomes than other workers. However, better-off workers are more likely than those who are more disadvantaged to be in higher-quality jobs. Thus, an important question is whether labor market success is due more to worker characteristics or initial job characteristics.

We isolated subgroup effects from others using multivariate regression methods. We estimated regression models for the three outcome measures used in the univariate subgroup analysis. In the main text, we present findings for the percentage who earned at least $10 at the last period we observed them, about 42 months after job start (Table V.5). The results for the other two outcomes are presented in Table D.6 and are qualitatively similar to those presented in the text (although a few differences exist). We present regression-adjusted means for each subgroup level and indicate whether the difference between the regression-adjusted means for each subgroup and the "left-out" subgroup is statistically significant at the five percent significance level.

 

Table V.5.
Multivariate Analysis Findings on the Percentage of Low-Wage Workers Earning
At Least $10 Three and a Half Years Later, By Gender and Model
Explanatory Variable Regression-Adjusted Means for Models with Demographic
and Other Denoted Explanatory Variables
Male Workers Female Workers
No Other Variables (1) Prepanel Work History Variables (2) Initial Job Variables (3) No Other Variables (1) Prepanel Work History Variables (2) Initial Job Variables (3)
Individual Characteristics
Age
   Younger than 20(a) 19 27 20 20 28 20
   20 to 29 30 32 30 17 17 17
   30 to 39 35** 34 36* 18 17 18
   40 to 49 32 26 32 14 14** 14
   50 or older 34 24 30 26 25 30
Race/Ethnicity
   White and other non-Hispanic(a) 32 32 32 19 19 19
   Black, non-Hispanic 23 25 25 17 18 17
   Hispanic 26 25 28 11* 11* 12
Educational Attainment
   Less than high school/GED(a) 19 20 22 11 12 13
   High school/GED 27 26 27 15 16 16
   Some college 39** 39** 34 21 20 19
   College graduate or more 47** 49** 44** 24** 23* 23
Has a Health Limitation
   No(a) 31 31 31 18 18 18
   Yes 24 25 27 11 11 13
Work Experience Prior to the Panel Period
Ever Worked for Six Straight Months
   No(a)   34     22  
   Yes   30     17  
Number of Years Ever Worked Six Straight Months
   Less than 5(a)   27     12  
   5 to 10   31     20  
   10 to 20   28     23*  
   More than 20   38     18  
Usually Worked at Least 35 Hours Per Week When Working
   No(a)   20     14  
   Yes   34**     20*  
Household Characteristics
Household Type
   Single adults with children(a) 33 34 32 19 19 20
   Married couples with children 36 36 34 20 20 20
   Married couples without children 28 27 31 13 13 12**
   Other adults without children 24 24 24 18 18 18
Household Income as a Percentage of the Federal Poverty Level
   100 percent or less(a) 29 27 30 11 12 13
   101 to 200 percent 31 32 32 14 15 15
   More than 200 percent 30 31 30 22** 22** 21
Received Public Assistance in the Past Year
   No(a) 31 32 31 18 18 17
   Yes 23 22* 28 19 19 20
Area Characteristics
Region of Residence
   Northeast(a) 27 27 29 22 21 22
   South 31 31 29 15 14 15
   Midwest 28 29 28 17 17 18
   West 33 33 36 22 22 19
Lives in a Metropolitan Area
   No 22 22 21 13 12 13
   Yes 34** 34** 34** 20** 20** 20*
20th Percentile of the Weekly Wage Distribution in State
   $250 or less(a) 30 30 30 18 18 18
   $251 to $269 37 35 34 17 17 17
   $270 or more 28 29 29 18 18 18
Percentage of State Population Residing in Metropolitan Areas
   72 or less(a) 24 24 25 22 22 23
   73 to 84 35** 35** 35* 15* 15 15**
   85 or more 33 33 32 16 16 16
Poverty Rate in State
   Less than 10 percent(a) 28 28 26 20 21 17
   10 to 12 percent 31 31 32 18 18 21
   More than 12 percent 31 32 32 15 15 15
Unemployment Rate in State
   6 percent or less(a) 31 31 30 17 17 17
   More than 6 percent 28 29 30 22 23 22
Change in Unemployment Rate in State of Residence Between 1996 and 1999 (Percentage Points)
   -2 percentage points or less(a) 21 19 19 14 14 14
   -1 to -2 percentage points 30 30 31 21 21 21
   More than -1 percentage point 35 36 36 13 13 13
Initial Job Characteristics
Hourly Wages
   Less than $5.00(a)     25     14
   $5.00 to $5.99     22     13
   $6.00 to $6.99     35     25**
   $7.00 to $7.50     42**     22
Usual Hours Worked per Week
   1 to 19(a)     29     14
   20 to 34     33     24**
   35 to 40     28     16
   More than 40     35     14
Has More than One Job or Business
   No(a)     31     17
   Yes     27     22
Owns Business (Self-Employed)
   No(a)     29     17
   Yes     41     29
Health Insurance Coverageb
   No(a)     27     16
   Yes     35*     19
Union Member
   No(a)     30     18
   Yes     32     20
Occupation
   Professional/technical(a)     34     19
   Sales/retail     30     23
   Administrative support/clerical     42     19
   Service professions/handlers/cleaners     25     13
   Machine/construction/production/ transportation     32     24
   Farm/agricultural/other workers     33     7*
Industry
   Agriculture/forestry/fishing and hunting(a)     20     12
   Mining/manufacturing/construction/ transportation and warehousing/ utilities     33     13
   Wholesale/retail trade     33     16
   Services/other     29     21
Type of Worker
   Continuous worker with only one employer/business     25     9
   Continuous worker with more than one employer/business     30     17*
   Intermittent worker, employed less than 75% of time     22     18*
   Intermittent worker, employed 75% or more of time     36*     23**
Sample Size 491 491 491 693 693 693
Source: 1996 SIPP longitudinal and wave 1 topical module files using the entry cohort sample of workers who started low-wage jobs within six months after the start of the panel period. All workers were followed for 42 months after job start.
Note: All figures are weighted using the 1996 calendar year weight.
a. Denotes the "omitted" explanatory variable in the regression model.
b. These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from other sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall health insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health insurance variable could not always be linked to the job under investigation, which led to a significant number of missing values. However, the subsets of health insurance variables overlap considerably: the source of health insurance coverage was the employer for 80 percent of those with any coverage.
* Difference between the variable mean and the mean of the "omitted" explanatory variable is significantly different from zero at the .10 level, two-tailed test.
** Difference between the variable mean and the mean of the "omitted" explanatory variable is significantly different from zero at the .05 level, two-tailed test.

We present estimates from three models for both males and females. The first model includes demographic variables only  that is, explanatory variables defined by individual, household, and area characteristics; model (1) on Table V.5. The second model includes demographic variables as well as prepanel work experience variables from the wave 1 topical module  model (2). The third model  model (3)  includes demographic variables and initial job-related variables. Table D.6 presents the model (3) results for the additional employment-related outcome measures only.

a. Models Including Demographic Variables Only

The regression-adjusted differences in labor market outcomes across subgroups defined by individual and household characteristics are largely similar to the univariate findings described above, although few findings are statistically significant (Table V.5). Again, the patterns of findings across demographic subgroups are similar to those observed for the aggregate analyses in Chapter IV, although fewer differences are statistically significant in the wage growth analysis.

Education is the strongest predictor of wage growth, especially for males, with college graduates more likely to experience wage growth than those with less education. Similar to the univariate subgroup findings, female Hispanic workers were significantly less likely than black non-Hispanics or white non-Hispanics to earn more than $10 per hour at the end of the follow-up period.

Living in a metropolitan area is a strong predictor of wage growth for both males and females. Holding all else constant, 34 percent of male low-wage workers in metropolitan areas were likely to earn more than $10 per hour at the last period, compared with only 22 percent among nonmetropolitan workers. However, most other explanatory variables measuring area characteristics had little predictive power in the regression models.

The regression R(2) value from model (1) is about .11 for males and .08 for females. Thus, demographic variables explain only about 10 percent of the variance in wage growth, and substantial residual factors remain that account for differences across workers.

b. Models Including Demographic and Prepanel Work Experience Measures

Most prepanel variables capturing prior work experience had only small effects on wage growth of low-wage workers. We observe some differences for female workers, with those who worked less than five years least likely to earn more than $10 per hour at the end of the study period. We also found that workers who typically worked full-time while employed prior to the panel period experienced better wage outcomes than part-time workers, and these differences were statistically significant for both males and females. The R-squared value in model (2) is about .14 for males and .10 for females, indicating that adding prepanel variables has only a small effect in explaining differences in wage growth across workers.

c. Models Including Demographic and Initial Job-Related Variables

The multivariate findings provide some evidence that job quality matters. Among low-wage male workers, those who had higher hourly wages in their initial job were more likely to be earning more than $10 per hour three years after job start. In addition, males in jobs with fringe benefits were also more likely to have higher hourly wages three years later. Among female

workers, those with lower starting wages and those who worked part-time (between 20 and 34 hours) in their initial job were more likely than those working fewer or more hours to earn $10 per hour or more at the time of the follow-up period  model (3) on Table IV.5.(44)

While those self-employed seem to do better, the differences are not statistically significant. Nor do we observe significant differences by industry and occupation. We also find that low-wage workers who stayed continuously in the same job over time were less likely to experience wage growth than those who switched jobs (either continuously moved from one job to another, or switched jobs with a break in between jobs but were employed over most of the follow-up period, Table V.5). Interestingly, these findings are strongest for intermittent workers who were employed at least 75 percent of the time.

In general, the inclusion of the job-related variables does not much affect the differences across the demographic subgroups as compared to those presented above. This is partly because few demographic variables were significant to begin with. However, race among females, and higher education for both groups, continue to remain important, although the effects of education are not statistically significant for females.

The inclusion of both the job and demographic characteristics yields a model R2 value of .18 for males and .14 for females (not shown). Thus, while including job characteristics helps explain some more of the differences in wage growth across groups of workers, substantial residual factors remain that account for differences in wage growth outcomes across low-wage workers, even after controlling for a large number of demographic and job-related factors. Clearly, there are other important factors that we could not identify using the SIPP data that may explain differences in wage growth outcomes across groups of workers.

Endnotes

(37) Medium-wage workers include those whose wages are between 100 and 200 percent of the federal poverty level, and high-wage workers are those whose wages are greater than 200 percent of the federal poverty level. The hourly wage cutoff for medium-wage workers is between $8.03 and $16.06 per hour. High-wage workers are those whose hourly wages are greater than $16.06 per hour.

(38) We chose to examine patterns of wage growth among those who started a job, as we wanted to know what wage growth welfare recipients and other low-wage workers who start a job might expect.

(39) As noted in Chapter II, the usual extent of data cleaning performed in earlier SIPP waves was not done for the 1996 longitudinal files.

(40) As described earlier, this six-month period refers to average wages during the first six-month period after the six-month period that was used to classify workers into low-, medium- or high-wage groups, which we called period 0. We do this because we are concerned about overstating wages which may be particularly low in period 0 for the reasons discussed earlier.

(41) Patterns of wage growth remain similar when we looked at alternative definitions of low-wage workers. For example, they remain similar when we use average wages across the first year to define low-wage workers, as well as when we exclude those with wages below $3.

(42) If we examine the change including the base period (period 0) used to classify workers into wage type, wage growth was somewhat higher (closer to 20 percent).

(43) We measured these indicators using information on the state in which the worker lived at the beginning and end of the follow-up period.

(44) Because the job variables are likely to be endogenous, they could lead to biased coefficient estimates on all the explanatory variables. Thus, we do not view our parameter estimates as "structural" relationships between the explanatory and dependent variables. Rather, our goal is to identify broad associations between subgroup variables and labor market outcomes.

 

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